Index

​RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response - Erlingsson et al., CCS '14
Randomized Response + Bloom Filter
​Deep Learning with Differential Privacy - Abadi et al., CCS '16 [Summary]
Discussed how to train deep neural networks with non-convex objectives, under a modest privacy budget
​Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning* - Hitaj et al., CCS '17
Proposed and implement an active inference attack on deep neural networks in a collaborative setting(which stresses the importance of using secure aggregation and differential privacy.)
​Opaque: An Oblivious and Encrypted Distributed Analytics Platform - Zheng et al., NSDI '17
​Prio: Private, Robust, and Scalable Computation of Aggregate Statistics - Corrigan-Gibbs et al., NSDI '17
​Honeycrisp: Large-Scale Differentially Private Aggregation Without a Trusted Core - Roth et al., SOSP '19
​Deep Leakage from Gradients - Zhu et al., NIPS '19 [Zhihu]
​Shredder: Learning Noise Distributions to Protect Inference Privacy - Mireshghallah et al., ASPLOS ' 20
​Fawkes: Protecting Privacy against Unauthorized Deep Learning Models - Shan et al., Security '20
​Orchard: Differentially Private Analytics at Scale - Roth- et al., OSDI '20
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Differential Privacy

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